Checking Fine and Gray subdistribution hazards model with cumulative sums of residuals. Lifetime Data Anal 2015 Apr;21(2):197-217
Date
11/26/2014Pubmed ID
25421251Pubmed Central ID
PMC4386671DOI
10.1007/s10985-014-9313-9Scopus ID
2-s2.0-84925543439 (requires institutional sign-in at Scopus site) 50 CitationsAbstract
Recently, Fine and Gray (J Am Stat Assoc 94:496-509, 1999) proposed a semi-parametric proportional regression model for the subdistribution hazard function which has been used extensively for analyzing competing risks data. However, failure of model adequacy could lead to severe bias in parameter estimation, and only a limited contribution has been made to check the model assumptions. In this paper, we present a class of analytical methods and graphical approaches for checking the assumptions of Fine and Gray's model. The proposed goodness-of-fit test procedures are based on the cumulative sums of residuals, which validate the model in three aspects: (1) proportionality of hazard ratio, (2) the linear functional form and (3) the link function. For each assumption testing, we provide a p-values and a visualized plot against the null hypothesis using a simulation-based approach. We also consider an omnibus test for overall evaluation against any model misspecification. The proposed tests perform well in simulation studies and are illustrated with two real data examples.
Author List
Li J, Scheike TH, Zhang MJAuthor
Mei-Jie Zhang PhD Professor in the Institute for Health and Equity department at Medical College of WisconsinMESH terms used to index this publication - Major topics in bold
BiasComputer Simulation
Data Interpretation, Statistical
Female
Humans
Leukemia, Myeloid, Acute
Linear Models
Liver Cirrhosis, Biliary
Male
Middle Aged
Proportional Hazards Models
Regression Analysis
